Different volumes and intensities of static stretching affect the range of motion and muscle force output in well-trained subjects
Bibliographic record
Abstract
The manipulation of the volume and intensity of static stretching (SS) can affect the range of motion (ROM) and muscle force output. The purpose of this study was to investigate the effect of two different SS protocols with different intensities (50% and 85% POD) and volumes (120-s and 240-s) on ROM, peak force, and muscle activity during maximal isometric leg curl exercise in well-trained participants. Fifteen young males (age:27.5 ± 6.1years, height:175.6 ± 4.7cm, and body mass:81.5 ± 10.4kg, 6 ± 2 years of resistance training experience) performed passive hip flexion with two different SS protocols: six stretches of 40-s, with 15-sec rest between each stretch at 50% of the point of discomfort (POD) and three stretches of 40-s, with 15-sec rest between each stretch at 85%POD. The passive hip flexion ROM, biceps femoris muscle activation (integrated electromyography: IEMG), and knee flexors force were monitored during a 3-s maximal voluntary isometric leg curl exercise. ROM increased between pre- and post-intervention for both SS protocols (50%POD: p = 0.016, Δ% = 4.6% and 85%POD: p < 0.001, Δ% = 11.42%). Peak force decreased between pre- and post-intervention only for 85%POD (p = 0.004, Δ% = 23.6%). There were no significant IEMG differences. In conclusion, both SS protocols increased ROM, however, the high-intensity and short-duration SS protocol decreased peak force.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".